Sentence Centrality Revisited for Unsupervised Summarization

@article{Zheng2019SentenceCR,
  title={Sentence Centrality Revisited for Unsupervised Summarization},
  author={Hao Zheng and Mirella Lapata},
  journal={ArXiv},
  year={2019},
  volume={abs/1906.03508}
}
Single document summarization has enjoyed renewed interest in recent years thanks to the popularity of neural network models and the availability of large-scale datasets. In this paper we develop an unsupervised approach arguing that it is unrealistic to expect large-scale and high-quality training data to be available or created for different types of summaries, domains, or languages. We revisit a popular graph-based ranking algorithm and modify how node (aka sentence) centrality is computed… 
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